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Magis, David; Beland, Sebastien; Raiche, Gilles – Applied Psychological Measurement, 2011
In this study, the estimation of extremely large or extremely small proficiency levels, given the item parameters of a logistic item response model, is investigated. On one hand, the estimation of proficiency levels by maximum likelihood (ML), despite being asymptotically unbiased, may yield infinite estimates. On the other hand, with an…
Descriptors: Test Length, Computation, Item Response Theory, Maximum Likelihood Statistics
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Magis, David; Raiche, Gilles – Applied Psychological Measurement, 2010
In this article the authors focus on the issue of the nonuniqueness of the maximum likelihood (ML) estimator of proficiency level in item response theory (with special attention to logistic models). The usual maximum a posteriori (MAP) method offers a good alternative within that framework; however, this article highlights some drawbacks of its…
Descriptors: Maximum Likelihood Statistics, Computation, Bayesian Statistics, Item Response Theory